Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model. Issue 1 (31st December 2017)

• Record Type: Journal Article
• Authors: Ito, Shin-ichi; Nagao, Hiromichi; Kasuya, Tadashi; Inoue, Junya
• Is Part Of: Science and technology of advanced materials. Volume 18:Issue 1(2017)
• Page Start: 857
• Page End: 868
• Publication Date: 2017-12-31
• Subjects: Grain growth -- data assimilation -- Bayesian statistics -- prediction method -- phase field model -- uncertainty quantification; 60 New topics / Others -- 404 Materials informatics / Genomics -- 403 CALPHAD / Phase field methods -- 400 Modeling / Simulations -- 500 Characterization; Materials -- Technological innovations -- Periodicals; 620.112
• DOI: 10.1080/14686996.2017.1378921
• Languages: English
• ISSNs: 1468-6996

Abstract

Abstract : We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design. Graphical Abstract: